Monolithic 3D Integration of Vertical Memory with Phototransistor for Near‐Sensor Cryptography and Homomorphic Data Searching

Abstract Inspired by the human retina, retinomorphic systems achieve efficient near‐sensor processing by tightly integrating sensing, memory, and computing. However, unlike biological vision, which evolved without selective pressure for data confidentiality, artificial edge systems face critical security demands. Therefore, next‐generation hardware must extend beyond biological mimicry by combining bio‐inspired efficiency with cryptographic capabilities. Here, a compact, multifunctional wafer‐scale monolithic 3D (M3D) architecture is proposed for secure in‐memory processing of optically acquired visual data. Integrating quantum dot‐sensitized phototransistors with stacked high‐density vertical resistive random‐access memories (VRRAMs) provides multi‐domain entropy sources, generating physical unclonable function (PUF) keys with ≈50% inter‐device variability. Multi‐layer encryption using functionally independent PUF keys enhances cryptographic resilience through key diversity. Concurrently, M3D ternary content‐addressable memory (TCAM) array, implemented with wide‐bandgap IGZO transistors, achieves high sensing margin (≈1.58 × 10 5 ), along with 9.61× area efficiency and 6.25× energy‐delay product improvements over planar designs. Notably, M3D sensory and TCAM systems support near‐sensor hashing and in‐memory Hamming distance computation directly on encrypted data, enabling application‐specific homomorphism with a 94.1% similarity preservation rate. Comparable classification accuracy for plaintext and encrypted hash inputs further underscores the potential of M3D‐integrated platforms for secure, privacy‐preserving machine vision at the edge.